Evaluation Dataset for the Symbolic Constraint-Guided Reasoning (SCGR) Framework

Finally, multi-dimensional quantitative assessments were conducted using models including GPT-3.5-Turbo, DeepSeek-V3, Qwen-Plus, GLM-4.5-Flash, and Hunyuan-Pro. The experimental environment is based on the Python 3.13 programming language, utilizing core tools such as Pandas for data processing, Scikit-learn for similarity measurement, and Scipy for statistical inference.The dataset covers a time span primarily from May to September 2025 during the experimental execution phase.

The data is organized in tabular format, encompassing model-generated reports, multi-dimensional scores for various systems across different models, semantic similarity results, t-test results, user evaluation data, and the final multi-model review results.The dataset structure is complete with no missing data. Regarding potential errors, since semantic similarity calculation relies on the pre-trained vector embedding model m3e-base, there may be model biases in capturing subtle nuances of specific medical terminology; however, these errors remain within statistically controlled ranges (e.g., p-values in the t-tests are significantly lower than 0.05, demonstrating high statistical significance).

Supporting data is stored within the supporting_data.zip archive.